Sign in to my dashboard Create an account

NetApp IT Perspective: A Hybrid Organizational Model for Data Science

Rajesh Shriyan

Data analytics are essential to planning and driving modern business. Furthermore, data scientists and their portfolio of methods and tools are the key to unlocking the most value from our data and improving our odds of success. But what is the right relationship between IT and business functions to best promote the healthy and scalable practice of data science in an enterprise? Is it best to have an analytics center of excellence?


The IT Enterprise Architecture (EA) team answered these questions by taking an organizational perspective based on NetApp’s culture, industry research, and conversations with external peers. Inside NetApp the EA team is responsible for setting IT enterprise technology directions and strategy, along with a forward-looking analytics organization.

Selecting where data scientists report

There are a few considerations when deciding to which functional organization data scientists should report. Data scientists need to possess or have access to those with an understanding of business processes, and a willingness to share information. They need easy access to “local” and cross-functional data and the authority to access it. They also need an IT-supported “onramp” to productionalize their models when ready to move past the incubation stage. Some possible organizational models include:

  • Data Science in Each Function: In this model, data scientists and IT are decentralized, with analysts in each function operating independently, but leveraging IT systems for their day-to-day needs.
  • Data Science in IT: The extreme opposite of having data scientists in each function is to have all data scientists centralized, operating within IT.
  • Data Science as a Separate Organization: In this model, data scientists are centralized and operate independently as a separate organization, requiring leadership under a Chief Data Officer.
  • Scattered Data Science Experts: As the name suggests, this model sees data scientists operating as decentralized resources in both specific business functions and within IT, working together to determine what needs to be done.
  • Data Science as a Center of Excellence (CoE): In this hybrid model, data scientists in the enterprise operate within specific business functions, but are united under an IT-led Center of Excellence that serves as a hub for all data science activity.
The Center of Excellence (CoE) Model

At NetApp, we decided the IT CoE model that complements data science practices in the business is the best approach. It is balanced, has a natural jump-off point from our current state at NetApp, and provides good checks-and-balances. Our model:

  • Provides visibility throughout the enterprise
  • Fosters communication and collaboration between business teams and IT
  • Prevents duplicated efforts through shared awareness of the larger data science project pipeline
  • Ensures appropriate access and compliance by establishing a data science authority within the enterprise to provide data governance
  • Shares and validates data models
  • Locates IT resources to help drive an IT technology roadmap developing a platform that best enables data science
  • Provides a resource loaner program for small business functions unable to hire dedicated resources
  • Establishes data science best practices

Center-of-Excellence-1024x512A data scientist is unlikely to succeed if they are not aligned to a business process expert. As analysts come up with new ways to innovate with data science, their productivity may be hindered without the ability to navigate business operations. This is another opportunity for the CoE to assist by providing direct access to operational experts, while aligning the process experience to the data scientist.


Being data literate is also important. Junior data scientists frequently lack knowledge on business processes and data beyond their own function. Yet, they need this knowledge to know how to meaningfully integrate their data with that of other teams. They also need to know of the potential pitfalls or nuances of data outside their business area. With a data and process literate team of data scientists under an IT-led CoE, more meaningful conversations can occur that bridge organizational boundaries.


In the new data science world, finding talent with a balanced skillset is challenging. It’s easy to imagine hiring “two-for-one” candidates who are both data scientists and process experts. But a more realistic approach is to recognize that data science is necessarily collaborative and a team sport. It makes sense to build actual and virtual teams with trained data science skills and augment them with business process SMEs and “data engineering” skills. This space is great for self-driven learning and a wealth of resources exist for motivated individuals to further develop their data science expertise.


The NetApp-on-NetApp blog series features advice from subject matter experts from NetApp IT who share their real-world experiences using NetApp’s industry-leading data management solutions to support business goals. Want to learn more about the program? Visit

Rajesh Shriyan

Rajesh Shriyan is the Director of IT Enterprise Architecture at NetApp. Rajesh and his team work with NetApp’s business teams to ensure application capability in the enterprise and map applications to these capabilities. They help determine how an organization can most effectively achieve its current and future objectives.

View all Posts by Rajesh Shriyan

Next Steps

Drift chat loading